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Search: subject:"long short-term memory"
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LSTM
Forecasting model
69
Prognoseverfahren
69
Neural networks
54
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54
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38
Theory
38
long short-term memory
31
Artificial intelligence
26
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26
Long short-term memory
22
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21
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21
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19
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19
deep learning
19
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18
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14
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11
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11
Machine learning
9
recurrent neural networks
9
machine learning
8
China
7
Long Short-Term Memory
7
Long short-term memory (LSTM)
6
Aktienindex
5
Mustererkennung
5
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forecasting
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Handmann, Uwe
2
Zengeler, Nico
2
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1
Alan Tan Wee Chiat
1
El-Omari, Nidhal Kamel Taha
1
Filipović, Damir
1
Foresti, Andrea
1
Ghosh, Soumadip
1
James, Praveen Edward
1
Khalilzadeh, Amir
1
Mennour, Hanene
1
Minami, Shotaro
1
Mootha, Siddartha
1
Mostefai, Sihem
1
Mun Hou Kit
1
Nag, Amitava
1
Pal, Subarno
1
Sridhar, Sashank
1
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1
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1
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International journal of networking and virtual organisations : IJNVO
3
International Journal of Synthetic Emotions (IJSE)
1
International journal of business information systems : IJBIS
1
International journal of intelligent enterprise
1
Journal of Risk and Financial Management
1
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1
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ECONIS (ZBW)
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1
Stock market manipulation detection using feature modelling with hybrid recurrent neural networks
Sridhar, Sashank
;
Mootha, Siddartha
- In:
International journal of networking and virtual …
26
(
2022
)
1/2
,
pp. 47-79
Persistent link: https://www.econbiz.de/10013256819
Saved in:
2
Machine learning for predicting stock return volatility
Filipović, Damir
;
Khalilzadeh, Amir
-
2021
distribution of stock returns. Finally, we show that our
long
short-term
memory
model outperforms other models by properly carrying …
Persistent link: https://www.econbiz.de/10012800743
Saved in:
3
Using deep learning methods in detecting the critical success factors on the implementation of cloud ERP
Zughoul, Basem
;
El-Omari, Nidhal Kamel Taha
;
Al-Refai, …
- In:
International journal of business information systems : …
44
(
2023
)
2
,
pp. 219-248
Persistent link: https://www.econbiz.de/10014437128
Saved in:
4
Research on long- and short-term music preference recommendation method integrating music emotional attention
Yang, Yan
- In:
International journal of networking and virtual …
28
(
2023
)
2/4
,
pp. 381-397
Persistent link: https://www.econbiz.de/10014436534
Saved in:
5
Contracts for difference: A reinforcement learning approach
Zengeler, Nico
;
Handmann, Uwe
- In:
Journal of Risk and Financial Management
13
(
2020
)
4
,
pp. 1-12
indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent
long
short-term
memory
…
Persistent link: https://www.econbiz.de/10012611307
Saved in:
6
Contracts for difference: a reinforcement learning approach
Zengeler, Nico
;
Handmann, Uwe
- In:
Journal of risk and financial management : JRFM
13
(
2020
)
4/78
,
pp. 1-12
indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent
long
short-term
memory
…
Persistent link: https://www.econbiz.de/10012302717
Saved in:
7
Deep learning-based distributed denial-of-service detection
Mennour, Hanene
;
Mostefai, Sihem
- In:
International journal of networking and virtual …
26
(
2022
)
1/2
,
pp. 80-103
Persistent link: https://www.econbiz.de/10013256822
Saved in:
8
Recurrent neural network-based speech recognition using MATLAB
James, Praveen Edward
;
Mun Hou Kit
;
Vaithilingam, …
- In:
International journal of intelligent enterprise
7
(
2020
)
1/2/3
,
pp. 56-66
Persistent link: https://www.econbiz.de/10012177243
Saved in:
9
Estimation of the ex ante distribution of returns for a portfolio of U.S. treasury securities via deep learning
Foresti, Andrea
-
2019
portfolio of U.S. Treasury securities. A
long
short-term
memory
model and a convolutional neural network are tested as the main …
Persistent link: https://www.econbiz.de/10012008287
Saved in:
10
Sentiment Analysis in the Light of LSTM Recurrent Neural Networks
Pal, Subarno
;
Ghosh, Soumadip
;
Nag, Amitava
- In:
International Journal of Synthetic Emotions (IJSE)
9
(
2018
)
1
,
pp. 33-39
Long
short-term
memory
(LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over …
Persistent link: https://www.econbiz.de/10012047577
Saved in:
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